PD-type iterative learning algorithm for uncertain time-delay systems

In this paper, for a class of NCS with uncertain time delay, a PD-type iterative learning algorithm (ILC) is proposed to compensate the time delay. Based on the strictly repetition of the initial state, the sufficient conditions which guarantee the uniform convergence of the ILC is given. And the limit output trajectories generated by the action of the ILC are also presented. Then, comparing with the efficiency of the P-type ILC algorithm, it is shown that the PD-type ILC is more effective to compensate the time delay. For the case that the range of the time delay becomes smaller, it can track the output trajectories more precisely than the P-type ILC algorithm. Moreover, under the same number of iteration, the PD-type ILC algorithm can track the state trajectories faster than the P-type ones.